Compare GSFA with MAST DEG results

Last updated: 2022-09-20

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Knit directory: GSFA_analysis/

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File	Version	Author	Date	Message
Rmd	b7a0c95	kevinlkx	2022-09-20	updated the size of the plots
html	74c9a61	kevinlkx	2022-09-07	Build site.
Rmd	d2ea088	kevinlkx	2022-09-07	edited section titles
html	cc94b95	kevinlkx	2022-09-07	Build site.
Rmd	b492ebf	kevinlkx	2022-09-07	removed legend in QQplots for GSFA only pvalues
html	c10b536	kevinlkx	2022-09-07	Build site.
Rmd	2c27043	kevinlkx	2022-09-07	added QQplots for all GSFA pvalues
html	d8a9602	kevinlkx	2022-09-05	Build site.
Rmd	b0c058d	kevinlkx	2022-09-05	updated qqplots for gsfa only
html	00603ad	kevinlkx	2022-09-03	Build site.
Rmd	9cd02e4	kevinlkx	2022-09-03	updated qq plots
html	7d40768	kevinlkx	2022-09-03	Build site.
Rmd	4af8f3b	kevinlkx	2022-09-03	added QQ plots for GSFA only genes

Load packages

suppressPackageStartupMessages(library(data.table))
suppressPackageStartupMessages(library(ggplot2))
require(reshape2)
require(dplyr)
theme_set(theme_bw() + theme(plot.title = element_text(size = 14, hjust = 0.5),
                             axis.title = element_text(size = 14),
                             axis.text = element_text(size = 13),
                             legend.title = element_text(size = 13),
                             legend.text = element_text(size = 12),
                             panel.grid.minor = element_blank())
)
library(ggvenn)
source("code/plotting_functions.R")

Set directories

res_dir <- "/project2/xinhe/kevinluo/GSFA/compare_with_mast/"
dir.create(res_dir, recursive = TRUE, showWarnings = FALSE)

LUHMES data

Load the output of GSFA fit_gsfa_multivar() run.

data_folder <- "/project2/xinhe/yifan/Factor_analysis/LUHMES/"
fit <- readRDS(paste0(data_folder,
                      "gsfa_output_detect_01/use_negctrl/All.gibbs_obj_k20.svd_negctrl.seed_14314.light.rds"))
gibbs_PM <- fit$posterior_means
lfsr_mat <- fit$lfsr[, -ncol(fit$lfsr)]
total_effect <- fit$total_effect[, -ncol(fit$total_effect)]
KO_names <- colnames(lfsr_mat)
guides <- KO_names[KO_names!="Nontargeting"]

DEGs detected by GSFA

lfsr_mat <- lfsr_mat[, guides]
gsfa_degs <- apply(lfsr_mat, 2, function(x){names(x)[x < 0.05]})
sapply(gsfa_degs, length)

  ADNP ARID1B  ASH1L   CHD2   CHD8 CTNND2 DYRK1A  HDAC5  MECP2  MYT1L   POGZ 
   795    310    322    756      0      0     23      0      0      0      0 
  PTEN   RELN  SETD5 
   895      0    466

Load MAST single-gene DE result

mast_list <- list()
for (m in guides){
  fname <- paste0(data_folder, "processed_data/MAST/dev_top6k_negctrl/gRNA_", m, ".dev_res_top6k.vs_negctrl.rds")
  tmp_df <- readRDS(fname)
  tmp_df$geneID <- rownames(tmp_df)
  tmp_df <- tmp_df %>% dplyr::rename(FDR = fdr, PValue = pval)
  mast_list[[m]] <- tmp_df
}
mast_signif_counts <- sapply(mast_list, function(x){filter(x, FDR < 0.05) %>% nrow()})

DEGs detected by MAST

mast_degs <- lapply(mast_list, function(x){rownames(x)[x$FDR < 0.05]})
sapply(mast_degs, length)

  ADNP ARID1B  ASH1L   CHD2   CHD8 CTNND2 DYRK1A  HDAC5  MECP2  MYT1L   POGZ 
    38     54     15     88      1      0      0      1      1      0      0 
  PTEN   RELN  SETD5 
   207      0      7

Compare DEGs from GSFA vs MAST

num_deg_guides.df <- data.frame()
for (m in guides){
  shared_degs <- intersect(mast_degs[[m]], gsfa_degs[[m]])
  mast_only_degs <- setdiff(mast_degs[[m]], gsfa_degs[[m]])
  gsfa_only_degs <- setdiff(gsfa_degs[[m]], mast_degs[[m]])
  num_deg_guides.df <- rbind(num_deg_guides.df, 
                             data.frame(guide = m, shared = length(shared_degs), mast_only = length(mast_only_degs), gsfa_only = length(gsfa_only_degs)))
}

all_mast_degs <- unique(unlist(mast_degs))
all_gsfa_degs <- unique(unlist(gsfa_degs))

length(all_gsfa_degs)
length(all_mast_degs)
length(setdiff(all_gsfa_degs, all_mast_degs))
length(setdiff(all_mast_degs, all_gsfa_degs))
length(intersect(all_gsfa_degs, all_mast_degs))

deg_list <- list("GSFA DEGs" = all_gsfa_degs,
                 "MAST DEGs" = all_mast_degs)

ggvenn(deg_list, fill_color = c("#00BA38","#619CFF"), show_percentage = FALSE, set_name_size = 6, text_size = 6)

Version	Author	Date
c10b536	kevinlkx	2022-09-07
7d40768	kevinlkx	2022-09-03

[1] 1366
[1] 323
[1] 1112
[1] 69
[1] 254

QQ plots comparing GSFA with MAST

combined_mast_res <- data.frame()
for(i in 1:length(guides)){
  guide <- guides[i]
  mast_res <- mast_list[[guide]]
  gsfa_de_genes <- gsfa_degs[[guide]]
  gsfa_de_genes <- intersect(gsfa_de_genes, rownames(mast_res))
  mast_res$gsfa_gene <- 0
  if(length(gsfa_de_genes) >0){
    mast_res[gsfa_de_genes, ]$gsfa_gene <- 1
  }
  combined_mast_res <- rbind(combined_mast_res, mast_res)
}

pvalue_list <- list('GSFA'=dplyr::filter(combined_mast_res,gsfa_gene==1)$PValue,
                    'all genes'=combined_mast_res$PValue)

qqplot.pvalue(pvalue_list, pointSize = 1, legendSize = 4) + 
      ggtitle("") + theme(plot.title = element_text(hjust = 0.5)) +
      scale_colour_discrete(name="Method") + 
      scale_color_manual(values=c("#00BA38","#619CFF"))

Version	Author	Date
c10b536	kevinlkx	2022-09-07
00603ad	kevinlkx	2022-09-03
7d40768	kevinlkx	2022-09-03

combined_mast_res <- data.frame()
for(m in guides){
  mast_res <- mast_list[[m]]
  curr_gsfa_degs <- gsfa_degs[[m]]
  curr_mast_degs <- mast_degs[[m]]
  gsfa_only_degs <- setdiff(curr_gsfa_degs, curr_mast_degs)
  mast_only_degs <- setdiff(curr_mast_degs, curr_gsfa_degs)

  mast_res$guide <- m
  mast_res$gsfa_gene <- 0
  if(length(curr_gsfa_degs) >0){
    mast_res[curr_gsfa_degs, ]$gsfa_gene <- 1
  }
  mast_res$gsfa_only_gene <- 0
  if(length(gsfa_only_degs) >0){
    mast_res[gsfa_only_degs, ]$gsfa_only_gene <- 1
  }
  mast_res$mast_only_gene <- 0
  if(length(mast_only_degs) >0){
    mast_res[mast_only_degs, ]$mast_only_gene <- 1
  }
  combined_mast_res <- rbind(combined_mast_res, mast_res)
}

pvalue_list <- list('GSFA'=dplyr::filter(combined_mast_res,gsfa_gene==1)$PValue,
                    'GSFA only'=dplyr::filter(combined_mast_res,gsfa_only_gene==1)$PValue,
                    'MAST only'=dplyr::filter(combined_mast_res,mast_only_gene==1)$PValue,
                    'all genes'=combined_mast_res$PValue)

qqplot.pvalue(pvalue_list, pointSize = 1, legendSize = 4) + 
  ggtitle("") + theme(plot.title = element_text(hjust = 0.5)) +
  scale_colour_discrete(name="Method")

Version	Author	Date
c10b536	kevinlkx	2022-09-07
00603ad	kevinlkx	2022-09-03
7d40768	kevinlkx	2022-09-03

QQ plots for GSFA p-values

combined_mast_res <- data.frame()
for(m in guides){
  mast_res <- mast_list[[m]]
  curr_gsfa_degs <- gsfa_degs[[m]]

  mast_res$gsfa_gene <- 0
  if(length(curr_gsfa_degs) >0){
    mast_res[curr_gsfa_degs, ]$gsfa_gene <- 1
  }
  
  combined_mast_res <- rbind(combined_mast_res, mast_res)
}

pvalue_list <- list('GSFA'=dplyr::filter(combined_mast_res,gsfa_gene==1)$PValue)

qqplot.pvalue(pvalue_list, pointSize = 1, legendSize = 4) + 
  ggtitle("") + theme(plot.title = element_text(hjust = 0.5)) +
  scale_color_manual(values=c("#00BA38")) +
  theme(legend.position="none")

Version	Author	Date
c10b536	kevinlkx	2022-09-07

QQ plots for GSFA only p-values

combined_mast_res <- data.frame()
for(m in guides){
  mast_res <- mast_list[[m]]
  curr_gsfa_degs <- gsfa_degs[[m]]
  curr_mast_degs <- mast_degs[[m]]
  gsfa_only_degs <- setdiff(curr_gsfa_degs, curr_mast_degs)

  mast_res$gsfa_only_gene <- 0
  if(length(gsfa_only_degs) >0){
    mast_res[gsfa_only_degs, ]$gsfa_only_gene <- 1
  }
  
  combined_mast_res <- rbind(combined_mast_res, mast_res)
}

pvalue_list <- list('GSFA only'=dplyr::filter(combined_mast_res,gsfa_only_gene==1)$PValue)

qqplot.pvalue(pvalue_list, pointSize = 1, legendSize = 4) + 
  ggtitle("") + theme(plot.title = element_text(hjust = 0.5)) +
  scale_color_manual(values=c("#00BA38")) +
  theme(legend.position="none")

Version	Author	Date
cc94b95	kevinlkx	2022-09-07
c10b536	kevinlkx	2022-09-07
d8a9602	kevinlkx	2022-09-05
00603ad	kevinlkx	2022-09-03
7d40768	kevinlkx	2022-09-03

T cell data